基于多突变点与模板匹配的用电设备在线状态监测方法

doi:10.16180/j.cnki.issn1007-7820.2024.06.009

摘要/Abstract

摘要：

针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法形成窗口维度和缓冲区维度的特征向量,通过两种维度的模板匹配实现用电设备的运行状态匹配和状态切换时刻定位。基于家用电冰箱的仿真实验结果表明,所提方法具有检测速度快、准确率高等优点,可为用电设备状态监测领域提供参考。

关键词: 大数据分析, 时序数据, 用电设备, 状态监测, 缓冲区模型, 多突变点检测, 滑动窗口, 模板匹配

Abstract:

In view of the problems such as slow processing speed and low accuracy of condition monitoring technology for electrical equipment at this stage, an online status monitoring method of electrical equipment based on the multi-change point detection technology and template matching strategy is proposed. Based on the buffer model and the sliding window model, this method uses the TSTKS(Ternary Search Tree and Kolmogorov-Smirnov) algorithm to form the feature vector of the window dimension and the buffer dimension. The running state matching and status switching time positioning of the electrical equipment are realized by template matching of the two dimensions. Simulation experiments results of the household refrigerator show that the proposed method has the advantages of fast detection speed and high accuracy, and can provide a reference for the state monitoring field of electrical equipment.

Key words: big data analysis, time series data, electrical equipment, condition monitoring, buffer model, multiple change points detection, sliding window, template matching

中图分类号:

TP311

贾灿, 齐金鹏, 袁傲, 薛宇鑫, 戴理. 基于多突变点与模板匹配的用电设备在线状态监测方法[J]. 电子科技, 2024, 37(6): 69-76.

JIA Can, QI Jinpeng, YUAN Ao, XUE Yuxin, DAI Li. An Online Condition Monitoring Method of Electrical Equipment Based on Multiple Change Points and Template Matching[J]. Electronic Science and Technology, 2024, 37(6): 69-76.

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参考文献 20

[1]	李学龙, 龚海刚. 大数据系统综述[J]. 中国科学(信息科学), 2015, 45(1):1-44.
	Li Xuelong, Gong Haigang. A survey on big data systems[J]. Science in China(Information Sciences), 2015, 45(1):1-44.
[2]	陈树勇, 宋书芳, 李兰欣, 等. 智能电网技术综述[J]. 电网技术, 2009, 33(8):1-7.
	Chen Shuyong, Song Shufang, Li Lanxin, et al. Survey on smart grid technology[J]. Power System Technology, 2009, 33(8):1-7.
[3]	张洁, 蔡然. 电力设备状态监测与故障诊断技术分析[J]. 电子技术, 2021, 50(12):274-275.
	Zhang Jie, Cai Ran. Analysis of power equipment condition monitoring and fault diagnosis technology[J]. Electronic Technology, 2021, 50(12):274-275.
[4]	孙丽玲, 许伯强, 李和明, 等. 基于小波分析的负荷突变在线监测方法[J]. 华北电力大学学报, 2001, 28(3):14-17.
	Sun Liling, Xu Boqiang, Li Heming, et al. Online load mutation monitoring based on wavelet analysis[J]. Journal of North China Electric Power University, 2001, 28(3):14-17.
[5]	许立, 张敬周, 赵要强, 等. 基于弹性滑窗FFT的用电设备在线状态监测研究与应用[J]. 计算机应用与软件, 2017, 34(1):90-95,128.
	Xu Li, Zhang Jingzhou, Zhao Yaoqiang, et al. Research and application of electrical equipment online conditin monitoring based on elastic sliding window FFT[J]. Computer Applications and Software, 2017, 34(1):90-95,128.
[6]	朱俊俊, 齐金鹏, 钟金美, 等. 一种基于随机交叠策略的多突变点在线检测方法[J]. 电子科技, 2022, 35(12):1-9.
	Zhu Junjun, Qi Jinpeng, Zhong Jinmei, et al. An online-method of multiple change points detection based on random and overlapping strategy[J]. Electronic Science and Technology, 2022, 35(12):1-9.
[7]	邹俊晨, 齐金鹏, 李娜, 等. 一种快速的突变点在线检测算法设计与实现[J]. 电子科技, 2020, 33(8):10-15,52.
	Zou Junchen, Qi Jinpeng, Li Na, et al. Design and implementation of a fast online algorithm for mutation point detection[J]. Electronic Science and Technology, 2020, 33(8):10-15,52.
[8]	王伟平, 李建中, 张冬冬, 等. 数据流上周期更新滑动窗口的连接算法[J]. 哈尔滨工业大学学报, 2005, 37(6):756-759,785.
	Wang Weiping, Li Jianzhong, Zhang Dongdong, et al. Periodically updating sliding window join algorithms over data streams[J]. Journal of Harbin Institute of Technology, 2005, 37(6):756-759,785.
[9]	田腾, 石茂林, 宋学官, 等. 基于滑动窗口的时间序列异常检测方法[J]. 仪表技术与传感器, 2021(7):112-116.
	Tian Teng, Shi Maolin, Song Xueguan, et al. Anomaly detecting method for time series based on sliding windows[J]. Instrument Technique and Sensor, 2021(7):112-116.
[10]	曹林, 于威威. 基于图像分割的自适应窗口双目立体匹配算法研究[J]. 计算机科学, 2021, 48(S2):314-318.
	Cao Lin, Yu Weiwei. Adaptive window binocular stereo matching algorithm based on image segmentation[J]. Computer Science, 2021, 48(S2):314-318.
[11]	曹一彤. 基于动态滑动窗口与模板匹配的病变数据专家系统模型构建[D]. 上海: 东华大学,2022:34-37.
	Cao Yitong. Construction of pathological data expert system model based on dynamic sliding window and template matching[D]. Shanghai: Donghua University,2022:34-37.
[12]	刘佳伦. 一种基于随机与自适应策略的多突变点检测方法[D]. 上海: 东华大学,2021:55-59.
	Liu Jialun. A multiple change points detection method based on random and adaptive strategy[D]. Shanghai: Donghua University,2021:55-59.
[13]	Qi J, Zhu Y, Pu F, et al. A novel RSW & TST framework of MCPs detection for abnormal pattern recognition on large-scale time series and pathological signalsin epilepsy[J]. Plos One, 2021, 16(12):110-117.
[14]	Qi J P, Zhang Q, Zhu Y, et al. A novel method for fast change-point detection on simulated time series and electrocardiogram data[J]. Plos One, 2014, 9(4):1-15.
[15]	宋巧红, 齐金鹏, 张煜. 基于多级Haar小波变换与KS统计的突变点快速探测方法[J]. 计算机工程, 2018, 44(5):14-18,24. doi: 10.19678/j.issn.1000-3428.0046777
	Song Qiaohong, Qi Jinpeng, Zhang Yu. Fast abrupt-point detection method based on multistage Haar wavelet transform and KS statistic[J]. Computer Engineering, 2018, 44(5):14-18,24. doi: 10.19678/j.issn.1000-3428.0046777
[16]	李娜, 齐金鹏, 邹俊晨, 等. 一种基于脑电波突变分析的癫痫疾病检测方法[J]. 信息技术, 2019, 43(12):41-46.
	Li Na, Qi Jinpeng, Zou Junchen, et al. An epilepsy detection method based on brain wave mutation analysis[J]. Information Technology, 2019, 43(12):41-46.
[17]	朱厚杰. 基于突变点检测的病理信号分析方法研究与应用[D]. 上海: 东华大学,2020:61-63.
	Zhu Houjie. A study and an application of pathological signal analysis method based on change point detection[D]. Shanghai: Donghua University,2020:61-63.
[18]	唐琎, 李青. 一种快速的模板匹配算法[J]. 计算机应用, 2010, 30(6):1559-1561,1564.
	Tang Jin, Li Qing. Fast template matching algorithm[J]. Journal of Computer Applications, 2010, 30(6):1559-1561,1564.
[19]	龚汉鑫, 齐金鹏, 孔凡书, 等. 一种基于模板匹配与隶属度解析的时序异常快速检测方法[J]. 电子科技, 2022, 35(6):1-5,27.
	Gong Hanxin, Qi Jinpeng, Kong Fanshu, et al. A fast time series anomaly detection method based on template matching and membership analysis[J]. Electronic Science and Technology, 2022, 35(6):1-5,27.
[20]	徐小良, 汪乐宇, 周泓. 有限状态机的一种实现框架[J]. 工程设计学报, 2003, 10(5):251-255.
	Xu Xiaoliang, Wang Leyu, Zhou Hong. Implementation framework of finite state machines[J]. Chinese Journal of Engineering Design, 2003, 10(5):251-255.s

WS	Time/s	Hit/%
2⁷	1.248 2	64.37
2⁸	0.638 9	72.41
2⁹	0.387 1	84.72
2¹⁰	0.180 7	48.16

交叠机制	Time/s	Hit/%
窗口不交叠、缓冲区不交叠	0.387 1	84.72
窗口交叠、缓冲区不交叠	0.469 7	88.13
窗口交叠、缓冲区交叠	0.514 2	93.46

冰箱状态	缓冲区大小BS	窗口大小WS
关机	2¹⁴	2¹¹
刚开机	2¹³	2¹⁰
制冷期	2¹²	2⁹
除霜期	2¹⁰	2⁹